Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land-atmosphere model
and specified observed sea surface temperature is compared to that for long multi-decade integrations
of the same model where the initial conditions are far removed from the seasons of validation. The
evaluations are performed for surface temperature and compared among all seasons. Skill is found
to be higher in the seasonal simulations than the multi-decadal integrations except during boreal
winter. The higher skill is prominent even beyond the first month when the direct influence of the
atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter
season for the dynamical seasonal forecasts, equal to that of the long integrations, which show some
of the highest skill during winter. The reason for the differences in skill during the non-winter
months is attributed to the severe climate drift in the long simulations, manifest through errors in
downward fluxes of water and energy over land and evident in soil wetness. The drift presses the
land surface to extreme dry or wet states over much of the globe, into a range where there is little
sensitivity of evaporation to fluctuations in soil moisture. Thus, the land-atmosphere feedback is
suppressed, which appears to lessen the model’s ability to respond correctly over land to remote
ocean temperature anomalies.